wzhe06/Ad-papers

Curated collection of academic papers and industry resources covering computational advertising fundamentals—optimization methods (FTRL, online learning), CTR prediction models (FM, deep learning architectures like DIEN), embedding techniques, and topic modeling for feature extraction. Organizes seminal works across categories including factorization machines, graph embeddings, and foundational distributed systems papers (Google's MapReduce, BigTable, HDFS). Actively maintained with implementation references and complementary resources like Spark MLlib CTR models and recommendation systems collections.

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